Nature tracks 100 most-cited scientific papers

After a researcher painstakingly collects the data, analyzes it, sweats over the manuscript that describes the findings, and finds a journal to publish it, a study that likely took years to conduct finally appears in public. Other researchers will read it and maybe get ideas for further research, and eventually cite the original article when publishing new findings.

The number of citations a paper receives is a way - though imperfect - of keeping track of its influence in any given scientific field. And so, Nature recently compiled a list of the most-cited papers of all those cataloged in Thomas Reuters’ Web of Science since 1900. The journal ran some nifty graphs related to the list, including a yearly breakdown of the number of citations for each paper.

The top paper, by biochemist Oliver Lowry, MD, PhD, garnered more than 300,000 citations since its publication in 1951. The last one on the list had just a little more than 12,000 citations. Many famous discoveries such as Watson and Crick’s description of DNA’s double helix aren't on the list, probably because those revolutionary findings quickly become well-known enough and authors didn’t consider citing the work necessary.

The story includes a mention of the work of a Stanford faculty member:

Number 41 on the list is a description of how to apply statistics to phylogenies. In 1984, evolutionary biologist Joe Felsenstein of the University of Washington in Seattle adapted a statistical tool known as the bootstrap to infer the accuracy of different parts of an evolutionary tree. The bootstrap involves resampling data from a set many times over, then using the variation in the resulting estimates to determine the confidence for individual branches. Although the paper was slow to amass citations, it rapidly grew in popularity in the 1990s and 2000s as molecular biologists recognized the need to attach such intervals to their predictions.

Felsenstein says that the concept of the bootstrap, devised in 1979 by Bradley Efron, a statistician at Stanford University in California, was much more fundamental than his work. But applying the method to a biological problem means it is cited by a much larger pool of researchers. His high citation count is also a consequence of how busy he was at the time, he says: he crammed everything into one paper rather than publishing multiple papers on the topic, which might have diluted the number of citations each one received. “I was unable to go off and write four more papers on the same thing,” he says. “I was too swamped to do that, not too principled.”

The article concludes with a description of some of the highlights in the fields of biological techniques, bioinformatics, phylogenetics, statistics, density fuctional theory and crystallography. It’s a nice look at some seminal findings that you won’t likely find in textbooks.